Reflexive reciprocity theory (RRT): Toward an aesthetic ecology of human–artificial intelligence learning

“We do not learn from experience . . . we learn from reflecting on experience.” — John Dewey (1933)Reflexive Reciprocity Theory (RRT), grounded in Conceptual Framework Theory (Maxwell, 2013; Ravitch & Riggan, 2017), positions learning as a reciprocal aesthetic process between human and artificial cognition. It contends that reflection, once confined to individual consciousness, becomes a mutual composition when extended through intelligent systems. At its core lies cognitive displacement—the redistribution of reflective work from individual minds into collaborative interaction spaces—serving as the theory’s central mechanism.Extending Dewey’s experiential pragmatism (1910, 1934, 1938), Eisner’s aesthetic epistemology (2002), Schön’s reflective practice (1983), Craig’s narrative inquiry (2003), and van Waveren’s reflective co-design (2024), RRT synthesizes philosophical traditions into a coherent analytical structure. Within this framework, cognition operates as dialogue: the human interprets the algorithm’s response while the machine adapts to human interpretation, creating a continuous exchange of perception and form. Reflexive reciprocity defines the dynamic through which both agents—human and machine—co-construct understanding, while cognitive displacement describes the aesthetic migration of agency across that exchange.Drawing on Dewey’s experiential philosophy, Eisner’s aesthetic rationality, and emerging research in human–AI interaction, RRT articulates an aesthetic ecology of learning—a setting where reflection and adaptation merge into shared creation. The framework builds on the author’s PedagoReLearn environment and related empirical vignettes, demonstrating how cognitive displacement manifests within adaptive AI–human systems.Implications extend to the design of adaptive tutoring platforms, teacher education, and cross-cultural pedagogy, reframing artificial intelligence not as automation but as co-interpretive partnership. The study concludes with a research agenda and dissemination plan for establishing RRT as recognized educational terminology—bridging humanistic and computational approaches to learning and design.Keywords: Reflexive Reciprocity Theory, Cognitive Displacement, Artificial Intelligence in Education, Reflective Practice, Aesthetic Learning, Human–AI Collaboration